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Recurrent Neural Networks Application to Forecasting with Two Cases: Load and Pollution

... learning, in the forecasting task. The forecasting models based on long short-term memory (LSTM) and gated recurrent unit (GRU) were established respectively, and the real data of power load and air pollution were verified. Compared with traditional machine learning algorithms, the simulation proves the superiority of the forecasting model based on RNN. © 2020, Springer Nature Switzerland AG. нет

Теги: deep learning , forecasting , gru , lstm , decision making , intelligent computing , learning algorithms , machine learning , pollution , forecasting modeling , forecasting models , forecasting problems , neural netwo
Раздел: ИСЭМ СО РАН
Energy balancing using charge/discharge storages control and load forecasts in a renewable-energy-based grids

Sidorov D., Tao Q., Muftahov I., Zhukov A., Karamov D., Dreglea A., Liu F. Energy balancing using charge/discharge storages control and load forecasts in a renewable-energy-based grids // Chinese Control Conference, CCC. Vol.2019-July. ID: 8865777. 2019. P.6865-6870. ISBN (print): 9789881563972. DOI: 10.23919/ChiCC.2019.8865777 Renewable-energy-based grids development needs new methods to maintain the balance between the load and generation using the efficient energy storages models. Most of the...

Теги: deep learning. , energy storage , forecasting , integral equations , inverse problem , machine learning , numerical methods , power systems , svm
Раздел: ИСЭМ СО РАН
Wind speed and power ultra short-term robust forecasting based on Takagi–Sugeno fuzzy model

... fuzzy model is obtained. Wind farms located in China (Shanxi Province) and in Ireland (County Kerry) are considered as cases with which to validate the proposed forecasting method. The forecasting results are compared with results from the contemporary machine learning-based models including support vector machine (SVM), the combined model of SVM and empirical mode decomposition, and back propagation neural network methods. The results show that the proposed T–S fuzzy model can effectively improve ...

Теги: linearization , machine learning , wind power: wind speed: t–s fuzzy model: forecasting , backpropagation , clustering algorithms , fuzzy clustering , learning systems , least squares approximations , neural networks , support vector machines , wind , wind power , b
Раздел: ИСЭМ СО РАН
Prediction of the power shortage in the electric power system by means of regression analysis by machine learning methods

Boyarkin D.A., Krupenev D.S., Iakubobsky D.V. Prediction of the power shortage in the electric power system by means of regression analysis by machine learning methods // E3S Web of Conferences. Т.114. ID: 03003. 2019. DOI: 10.1051/e3sconf/201911403003 Modern electricity consumers place increasingly high demands on the level of reliability of power supply and, correspondingly, the reliability ...

Теги: decision trees , electric power systems , machine learning , number theory , random number generation , regression analysis , software reliability , support vector machines , electric power systems (eps) , mac
Раздел: ИСЭМ СО РАН
Intelligent control of a wind turbine based on reinforcement learning

Tomin N., Kurbatsky V., Guliyev H. Intelligent control of a wind turbine based on reinforcement learning // 2019 16th Conference on Electrical Machines, Drives and Power Systems, ELMA 2019 - Proceedings. ID: 8771645. 2019. ISBN (print): 9781728114132. DOI: 10.1109/ELMA.2019.8771645 Advanced controllers of modern wind turbines can help increase energy capture efficiency and reduce structural loading. However, to fulfill the modern wind turbine control demands with contradicting requirements (efficiency...

Теги: control , mimo control , pitch control , reinforcement learning , torque control , wind turbine , adaptive control systems , control engineering , electric machinery , machine learning , mimo systems , stochastic systems , wind , wind turbines , adaptive control des
Раздел: ИСЭМ СО РАН
The development of a joint modelling framework for operational flexibility in power systems

Voropai N., Rehtanz C., Kippelt S., Tomin N., Haeger U., Efimov D., Kurbatsky V., Kolosok I. The development of a joint modelling framework for operational flexibility in power systems // 2019 16th Conference on Electrical Machines, Drives and Power Systems, ELMA 2019 - Proceedings. ID: 8771685. 2019. ISBN (print): 9781728114132. DOI: 10.1109/ELMA.2019.8771685 The TU Dortmund University (Germany) and the Energy Systems Institute of the Russian Academy of Sciences (Russia) launched a joint research...

Теги: artificial intelligence , electric power system , flexibility , machine learning , power system security , electric machinery , electric power systems , learning systems , different layers , energy systems , modelling framework , operational flexibility , russian aca
Раздел: ИСЭМ СО РАН
Russian-Chinese Workshop "Mathematical Modeling of Renewable and Isolated Hybrid Power Systems"

... Optimization Methods in ESI RAS / SEI Ac. Sc. USSR (survey) More talks TBA Apart from Plenary/Section Sessions, the programm will include the Technical Tour to the Corporate Educational and Research Center of JSC "Irkutskenego", round table on Machine Learning & AI, NSFC Project Meeting and International science and technology cooperation program Project Meeting. The scientific tour to the Limnology Museum of RAS (Listvyanka, lake Baikal) is scheduled.

Теги: power systems mathematical modeling and control , forecasting , isolated hybrid power systems , wind ramp prediction , machine learning
Раздел: ИСЭМ СО РАН
Voltage/VAR Control and Optimization: AI approach

... bear, so special attentions are paid to the application of AI techniques in reactive voltage control and a lot of results in this field are obtained by many authors. This paper presents a hybrid Volt/VAr control approach based on AI techniques such as machine learning and multi-agent systems based models. Proposed approach enjoys high efficiency for various scenarios of modified IEEE 6-Bus and 118-Bus test systems. © 2018 входит

Теги: machine learning , multi-agent system , power system , random forest , security , volt-var control , decision trees , intelligent agents , learning systems , value engineering , voltage regulators , inherent characteristics , optimal solutions , random forests , tradit
Раздел: ИСЭМ СО РАН
Использование методов машинного обучения при оценке надёжности электроэнергетических систем методом Монте-Карло

Бояркин Д.А., Крупенев Д.С., Якубовский Д.В. Использование методов машинного обучения при оценке надёжности электроэнергетических систем методом Монте-Карло // Вестник ЮУрГУ. Серия «Математическое моделирование и программирование». Т.11. №4. 2018. C.146-153. DOI: 10/14529/mmp18041 В статье рассматривается вопрос повышения вычислительной эффективности процедуры оценки балансовой надежности электроэнергетических систем при использовании метода статистических испытаний (метод Монте-Карло). При использовании...

Теги: электроэнергетические системы , оценка надежности , метод монте-карло , машинное обучение , electric power systems , adequacy assessment , monte carlo method , machine learning
Раздел: ИСЭМ СО РАН
Machine learning in electric power systems adequacy assessment using Monte-Carlo method

Boyarkin D.A., Krupenev D.S., Iakubovskii D.V. Machine learning in electric power systems adequacy assessment using Monte-Carlo method // Bulletin of the South Ural State University, Series: Mathematical Modelling, Programming and Computer Software. Vol.11. No.4. 2018. P.146-153. DOI: 10.14529/mmp180411 ...

Теги: adequacy assessment , electric power systems , machine learning , monte carlo method
Раздел: ИСЭМ СО РАН


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